首页id3决策树python调包实现

id3决策树python调包实现

时间: 2023-12-19 10:30:35 浏览: 84

可以使用Python中的scikit-learn库来实现ID3决策树。具体步骤如下： 1.导入需要的库和数据集 ```python from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split # 加载数据集 iris = datasets.load_iris() X = iris.data y = iris.target ``` 2.将数据集分为训集和测试集 ```python X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) ``` 3.创建决策树模型并进行训练 ```python # 创建决策树模型 clf = DecisionTreeClassifier(criterion='entropy') # 训练模型 clf.fit(X_train, y_train) ``` 4.使用测试集进行预测并计算准确率 ```python # 预测测试集 y_pred = clf.predict(X_test) # 计算准确率 accuracy = clf.score(X_test, y_test) print('Accuracy:', accuracy) ``` 完整代码如下： ```python from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split # 加载数据集 iris = datasets.load_iris() X = iris.data y = iris.target # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 创建决策树模型 clf = DecisionTreeClassifier(criterion='entropy') # 训练模型 clf.fit(X_train, y_train) # 预测测试集 y_pred = clf.predict(X_test) # 计算准确率 accuracy = clf.score(X_test, y_test) print('Accuracy:', accuracy) ```

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